Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	140,651
2	Rhode Island	139,330
3	South Dakota	138,356
4	Utah	123,702
5	Tennessee	121,634
6	Arizona	118,258
7	Iowa	115,380
8	Nebraska	113,389
9	Wisconsin	113,167
10	Oklahoma	113,127
11	South Carolina	111,996
12	New Jersey	111,810
13	Arkansas	111,103
14	Alabama	107,498
15	Indiana	107,078
16	Delaware	106,641
17	Kansas	106,628
18	Illinois	105,122
19	Idaho	104,835
20	New York	104,796
21	Mississippi	104,663
22	Florida	103,481
23	Nevada	102,097
24	Montana	101,609
25	Minnesota	101,443
26	Georgia	101,056
27	Wyoming	100,212
28	Kentucky	99,826
29	Massachusetts	99,563
30	Texas	99,554
31	Louisiana	98,375
32	Missouri	97,661
33	Connecticut	94,792
34	California	94,609
35	New Mexico	94,055
36	Michigan	92,897
37	North Carolina	92,734
38	Alaska	91,983
39	Ohio	91,451
40	Pennsylvania	89,706
41	Colorado	88,375
42	West Virginia	84,982
43	Virginia	77,015
44	Maryland	73,718
45	New Hampshire	69,430
46	District of Columbia	67,351
47	Washington	52,790
48	Puerto Rico	51,343
49	Maine	45,149
50	Oregon	43,245
51	Vermont	36,415
52	Hawaii	22,929

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	554
2	Rhode Island	401
3	Alaska	351
4	Connecticut	298
5	North Carolina	277
6	Pennsylvania	257
7	Colorado	256
8	Maine	226
9	Minnesota	223
10	Washington	218
11	Florida	216
12	Delaware	212
13	North Dakota	203
14	Puerto Rico	198
15	Illinois	196
16	Idaho	195
17	Tennessee	194
18	Nevada	176
19	Wyoming	174
20	West Virginia	170
21	Massachusetts	167
22	New York	160
23	New Mexico	158
24	South Dakota	155
25	New Hampshire	143
26	Texas	141
27	Indiana	137
28	Oregon	134
29	Montana	133
30	Ohio	133
31	Kentucky	132
32	Mississippi	128
33	Wisconsin	128
34	Louisiana	127
35	Kansas	125
36	Maryland	123
37	Virginia	123
38	Iowa	117
39	Utah	113
40	Georgia	108
41	South Carolina	107
42	Nebraska	104
43	Missouri	100
44	District of Columbia	99
45	Arizona	93
46	Oklahoma	91
47	Vermont	78
48	Alabama	64
49	Arkansas	64
50	California	55
51	Hawaii	42
52	New Jersey	-179

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,870
2	New York	2,652
3	Massachusetts	2,550
4	Rhode Island	2,518
5	Mississippi	2,416
6	Arizona	2,374
7	Connecticut	2,266
8	Louisiana	2,230
9	Alabama	2,218
10	South Dakota	2,217
11	Pennsylvania	2,045
12	North Dakota	1,990
13	Indiana	1,977
14	New Mexico	1,932
15	Illinois	1,910
16	Arkansas	1,897
17	Iowa	1,879
18	Michigan	1,861
19	South Carolina	1,839
20	Georgia	1,834
21	Nevada	1,768
22	Tennessee	1,768
23	Texas	1,733
24	Kansas	1,723
25	Oklahoma	1,715
26	Delaware	1,665
27	Ohio	1,641
28	Florida	1,630
29	District of Columbia	1,564
30	California	1,558
31	Missouri	1,503
32	West Virginia	1,491
33	Kentucky	1,477
34	Montana	1,466
35	Maryland	1,440
36	Wisconsin	1,293
37	Minnesota	1,275
38	Virginia	1,263
39	Nebraska	1,222
40	Wyoming	1,221
41	North Carolina	1,205
42	Idaho	1,145
43	Colorado	1,112
44	New Hampshire	953
45	Washington	725
46	Puerto Rico	715
47	Utah	683
48	Oregon	593
49	Maine	578
50	Alaska	451
51	Vermont	394
52	Hawaii	338

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	6
2	Oklahoma	6
3	Alaska	5
4	New Jersey	4
5	New Mexico	4
6	Connecticut	3
7	Georgia	3
8	Kentucky	3
9	Pennsylvania	3
10	Virginia	3
11	Wisconsin	3
12	Delaware	2
13	District of Columbia	2
14	Florida	2
15	Idaho	2
16	Louisiana	2
17	Maryland	2
18	Minnesota	2
19	Mississippi	2
20	Missouri	2
21	Nevada	2
22	New Hampshire	2
23	New York	2
24	North Carolina	2
25	Puerto Rico	2
26	Rhode Island	2
27	South Carolina	2
28	Alabama	1
29	California	1
30	Colorado	1
31	Illinois	1
32	Indiana	1
33	Kansas	1
34	Maine	1
35	Massachusetts	1
36	Montana	1
37	Nebraska	1
38	Ohio	1
39	South Dakota	1
40	Tennessee	1
41	Texas	1
42	Vermont	1
43	Washington	1
44	Wyoming	1
45	Arizona	0
46	Arkansas	0
47	Hawaii	0
48	Iowa	0
49	North Dakota	0
50	Oregon	0
51	Utah	0
52	West Virginia	-27

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	357,752	1	99
Crowley	Colorado	356,542	2	99
Bent	Colorado	271,293	3	99
Dewey	South Dakota	248,133	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,069	187	94
Richland	South Carolina	110,172	994	68
York	South Carolina	108,353	1069	65
Orange	California	85,005	2168	30
Pierce	Washington	53,801	2878	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,557	1825	41
Davidson	Tennessee	1,338	2076	33
York	South Carolina	1,338	2078	33
Richland	South Carolina	1,306	2113	32
Pierce	Washington	713	2740	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.